Accurate Blind Predictions of OpenFOAM Energy Consumption Using the LBM Prediction Model

  • Davide Morelli
  • Antonio Cisternino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8806)


The ability to predict the energy consumption of an HPC task, varying the number of assigned nodes, can lead to the ability to assign the correct number of nodes to tasks, saving large amount of energy.

In this paper we present LBM, a model capable of predicting the resource usage (applicable to different resources, such as completion time and energy consumption) of programs, following a black box approach, where only passive measures of the running program are used to build the prediction model, without requiring its source code, or static analysis of the binary. LBM builds the predicting model using other programs as benchmarks. We tested LBM predicting the energy consumption of pitzDaily, a case of the OpenFOAM CFD suite, using a very low number of benchmarks (3), obtaining extremely precise predictions.


Energy Consumption Completion Time Resource Consumption Target Program Target Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Davide Morelli
    • 1
  • Antonio Cisternino
    • 1
  1. 1.Computer Science DepartmentUniversity of PisaPisaItaly

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